The universal approximation theorem in neural networks [1]} states that feed-forward networks with a single hidden layer containing a sufficient number of neurons and sufficient training data can model any function to a given level of accuracy (in our scenario, any 2D mask function). An instance segmentation application based on the universal approximation theorem implies that we can theoretically approximate any instance shape as long as we use a sufficiently-large hidden layer.
